Biology subjects

James, B.

Publications and source records attributed to James, B..

4 recordsLinked to original sources

bioRxiv2018

The BRadykinesia Akinesia INcoordination (BRAIN) tap test: capturing the sequence effect

BackgroundThe BRAIN tap test is an online keyboard tapping task that has been previously validated to assess upper limb motor function in Parkinsons disease (PD).\n\nObjectivesTo develop a new parameter which detects a sequence effect and to reliably distinguish between PD patients on and off medication. Alongside, we sought to validate a mobile version of the test for use on smartphones and tablet devices.\n\nMethodsBRAIN test scores in 61 patients with PD and 93 healthy controls were compared. A range of established parameters captured speed and accuracy of alternate taps. The new VS (Velocity Score) recorded the inter-tap speed. Decrement in the VS was used as a marker for the sequence effect. In the validation phase, 19 PD patients and 19 controls were tested using multiple types of hardware platforms including smart devices.\n\nResultsQuantified slopes from the VS demonstrated bradykinesia (sequence effect) in PD patients (slope cut-off -0.002) with sensitivity of 58% and specificity of 81% (discovery phase of the study) and sensitivity of 65% and specificity of 88% (validation phase). All BRAIN test parameters differentiated between on medication and off medication states in PD. Most BRAIN tap test parameters had high test-retest reliability values (ICC>0.75). Differentiation between PD patients and controls was possible on all hardware versions of the test.\n\nConclusionThe BRAIN tap test is a simple, user-friendly and free-to-use tool for assessment of upper limb motor dysfunction in PD, which now includes a measure of bradykinesia.

neuroscience↗

bioRxiv2018

MeShClust2: Application of alignment-free identity scores in clustering long DNA sequences

Grouping sequences into similar clusters is an important part of sequence analysis. Widely used clustering tools sacrifice quality for speed. Previously, we developed MeShClust, which utilizes k-mer counts in an alignment-assisted classifier and the mean-shift algorithm for clustering DNA sequences. Although MeShClust outperformed related tools in terms of cluster quality, the alignment algorithm used for generating training data for the classifier was not scalable to longer sequences. In contrast, MeShClust2 generates semi-synthetic sequence pairs with known mutation rates, avoiding alignment algorithms. MeShClust2clustered 3600 bacterial genomes, providing a utility for clustering long sequences using identity scores for the first time.

bioinformatics↗

bioRxiv2018

An amplitude code increases the efficiency of information transmission across a visual synapse

Most neurons in the brain transmit information digitally using sequences of spikes that trigger release of synaptic vesicles of fixed size. The first stages of vision and hearing are distinct in operating with analogue signals, but it is unclear how these are recoded for synaptic transmission. By imaging the release of glutamate in live zebrafish, we demonstrate how ribbon synapses of retinal bipolar cells transmit analogue visual signals by changes in both the rate and amplitude of synaptic events. Higher contrasts released glutamate packets composed of more vesicles and coding by amplitude often continued after rate coding had saturated. Glutamate packets equivalent to five vesicles transmitted four times as many bits of information per vesicle compared to independent release events. By discretizing analogue signals into sequences of numbers ranging up to eleven, ribbon synapses increase the dynamic range, temporal precision and efficiency with which visual information is transmitted.

neuroscience↗

bioRxiv2018

Spikeling: a low-cost hardware implementation of a spiking neuron for neuroscience teaching and outreach.

Understanding of how neurons encode and compute information is fundamental to our study of the brain, but opportunities for hands-on experience with neurophysiological techniques on live neurons are scarce in science education. Here, we present Spikeling, an open source {pound}25 in silico implementation of a spiking neuron that mimics a wide range of neuronal behaviours for classroom education and public neuroscience outreach. Spikeling is based on an Arduino microcontroller running the computationally efficient Izhikevich model of a spiking neuron. The microcontroller is connected to input ports that simulate synaptic excitation or inhibition, dials controlling current injection and noise levels, a photodiode that makes Spikeling light-sensitive and an LED and speaker that allows spikes to be seen and heard. Output ports provide access to variables such as membrane potential for recording in experiments or digital signals that can be used to excite other connected Spikelings. These features allow for the intuitive exploration of the function of neurons and networks. We also report our experience of using Spikeling as a teaching tool for undergraduate and graduate neuroscience education in Nigeria and the UK.

scientific communication and education↗